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Dataset results
83 results for “Marker Detection”
Supplementary dataset to publication: Oxford nanopore technologies - a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella, Thomas et al 2023
<p>Bacteria of the genus <em>Salmonella</em> pose a major risk to livestock, the food economy, and public health. <em>Salmonella</em> infections are one of the leading causes of food poisoning. The identification of serovars of <em>Salmonella</em> achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by <em>in silico</em> serotyping has been established as an alternative method for serotyping and the detection of genetic markers for <em>Salmonella</em>. Until now, WGS data generated with Illumina sequencing are used to validate <em>in silico</em> serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 <em>Salmonella</em> strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the <em>in silico</em> serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, <em>in silico</em> serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for <em>Salmonella in silico</em> serotyping and genetic marker detection.</p>
Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes
<p>DNA metabarcoding is used to enumerate and identify taxa in both environmental samples and tissue mixtures, but the effectiveness of particular markers depends on their sensitivity to the taxa involved. Using multiple primer sets that amplify different genes can mitigate biases in amplification efficiency, sequence resolution, and reference data availability, but few empirical studies have evaluated markers for complementary performance. Here, we assess the individual and joint performance of 22 markers for detecting species in a DNA pool of 98 species of marine and freshwater bony fishes from geographically and phylogenetically diverse origins. We find that a portfolio of four markers targeting 12S, 16S, and two regions of COI identifies 100% of reference taxa to family and nearly 60% to species. We then use these four markers to evaluate metabarcoding of heterogeneous tissue mixtures, using experimental fishmeal to test: 1) the tissue input threshold to ensure detection; 2) how read depth scales with tissue abundance; and 3) the effect of non-target material in the mixture on recovery of target taxa. We consistently detect taxa that make up >1% of fishmeal mixtures and can detect taxa at the lowest input level of 0.01%, but rare taxa (<1%) were detected inconsistently across markers and replicates. Read counts showed only a weak correlation with tissue input, suggesting they are not a reliable quantitative proxy for relative abundance. Despite the limitations arising from primer specificity and reference data availability, our results demonstrate that a modest portfolio of markers can perform well in detecting and identifying aquatic species in complex mixtures despite heterogeneity in tissue representation, phylogenetic affinities, and from a broad geographic range.</p>
Data for: Optimizing a metabarcoding marker portfolio for species detection from complex mixtures of globally diverse fishes
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Modeling Mood Course to Detect Markers of Effective Adaptive Interventions
ClinicalTrials.gov study NCT03358238. IPD Sharing: NO. Countries: 1. Publications: 2.
Modeling Mood Course to Detect Markers for Effective Adaptive Interventions- Aim 3
ClinicalTrials.gov study NCT04098497. IPD Sharing: NO. Countries: 1. Publications: 2.
Substudy 'B' of the Accuracy of Ingestible Event Marker (IEM) Detection by the Medical Information Device #1 (MIND1)
ClinicalTrials.gov study NCT02404532. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Substudy of the Accuracy of Ingestible Event Marker (IEM) Detection by the Medical Information Device #1 (MIND1)
ClinicalTrials.gov study NCT02091882. IPD Sharing: YES. Countries: 1. Publications: 3.
Data from: Comparative patterns of temporal decay and detectability of eDNA and eRNA across molecular markers in connected and isolated freshwater mesocosms using digital PCR
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Construction of genetic linkage map based on SNP markers, QTL mapping and detection of candidate genes of growth-related traits in Pacific abalone using genotyping-by-sequencing
<p><a name="_Hlk72585736"><span>Pacific abalone (<i>Haliotis discus hannai</i>) is a commercially important high valued molluscan species. Its wild population has decreased in recent years. Pacific abalone is widely cultured in Korea. Traditional breeding programs have been implemented for hatchery production of abalone seeds. To obtain more genetic information for the molecular breeding program, a high-density linkage map and quantitative trait locus (QTL) for three growth-related traits was constructed for Pacific abalone. F1 cross population with two parents were sampled to construct the linkage map using genotyping by sequencing (GBS). A total of 664,630,534 clean reads and 56,686 SNPs were generated. In sum, 3,345 segregating SNPs were used to construct a consensus linkage map. The map spanned 1,747.023 cM with 18 linkage groups and an average interval of 0.55 cM. QTL analysis revealed two significant QTL in LG10 on the consensus linkage map in each growth-related trait. Both the QTLs are located in the telomere region of the chromosome. Moreover, four potential candidate genes for growth-related traits were identified in the QTL region. Expression analysis revealed that identified genes are involved in growth regulation of abalone. The newly constructed genetic linkage map, growth-related QTLs and potential candidate genes identified in the present study can be used as valuable genetic resources and will be useful for marker-assisted selection (MAS) of Pacific abalone in molecular breeding program.</span></a></p>
Source data of scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
<p>The source data of the manuscript: scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.</p>
Smartphone Enabled Detection of Nocturnal Cough Rate and Sleep Quality as a Prognostic Marker for Asthma Control
ClinicalTrials.gov study NCT03635710. IPD Sharing: NO. Countries: 1. Publications: 1.
Gastric Cancer Marker Detection and Its Kit Development
ClinicalTrials.gov study NCT05010863. IPD Sharing: NO. Countries: 1. Publications: 16.
Mayo Designed Soft Tissue Ultrasound-Detectable Marker
ClinicalTrials.gov study NCT04674852. IPD Sharing: NO. Countries: 1. Publications: 1.
Exploration of DNA Methylation as a Marker for Early Detection of High Grade Serous Epithelial Ovarian Cancer
ClinicalTrials.gov study NCT03622385. IPD Sharing: NO. Countries: 1. Publications: 8.
Validation of the IgA1 Detection Method With Gradient Glycosylation by Mass Spectrometry as a Potential Marker of Renal Involvement in Pediatric Rheumatoid Purpura
ClinicalTrials.gov study NCT04655378. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Development of a Next Generation Sequencing (NGS) -Based Assay to Detect Preeclampsia Molecular Markers
ClinicalTrials.gov study NCT02808494. IPD Sharing: NO. Countries: 1. Publications: 1.
Kidney and Intestinal Markers for Early Detection of Organ Injury After Endovascular Aortic Repair
ClinicalTrials.gov study NCT01915446. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Detection of Occult Paroxysmal AF in Cryptogenic Stroke or TIA Patients Using an Implantable Loop Recorder and Correlation With Genetic Markers.
ClinicalTrials.gov study NCT02216370. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Value of sTREM-1, PCT, and CRP as Markers for the Detection of Sepsis and Bacteremia Among Patients With a FUO
ClinicalTrials.gov study NCT01410578. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Detecting Markers of Kidney Function With Intravenous Microdialysis
ClinicalTrials.gov study NCT03159806. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.